Chronic kidney diseases (CKD) such as kidney fibrosis are global health challenges. In the US alone CKD cost Medicare an estimated 50 billion dollars for patients with CKD age 65 and older in 2013. Recent studies revealed that after AKI renal epithelial cells undergo a partial epithelial-to-mesenchymal transition (pEMT) and G2/M cell cycle arrest through a Snail1-Twist1-p21 axis; these cells secrete profibrotic factors and contribute to fibrosis progression. Therefore these three factors become promising potential drug targets for treating fibrosis, but further development requires addressing several outstanding open questions. The temporal sequence and causal relation between pEMT and cell cycle arrest is controversial, and the respective roles of Snail1, Twist1, and p21 on regulating pEMT and cell cycle arrest is unclear. Addressing these questions requires quantitative systems biology approaches beyond cell biology methods traditionally used in the field. In recent years my lab has made progression on deep learning based image automated analysis for live cell images, CRISPR-based gene editing, and mathematical modeling and other quantitative biology tools. These technological developments position us to tackle the above-mentioned challenging questions related to kidney fibrosis. Based on existing studies and our preliminary results, we hypothesize that there is a temporal order of the three factors, with p21 initializing G2/M arrest, which is reinforced by subsequent upregulation of Snail1; Snail1 also activates, and Twist1 further maintains the pEMT program; due to their temporally varying roles, effectiveness of targeting these factors depends on the timing of treatment. We will test the hypothesis with quantitative imaging studies using established cell lines and primary renal epithelial cells and mathematical analysis of competing models. In Aim 1, we will perform multi-color flow cytometry studies and time-lapse imaging studies on progression of cell cycle, EMT, and other cell fates of cells under stimulation. The two types of studies will provide complementary information on whether pEMT and cell cycle are tightly coupled, and will map out the temporal sequence of events of various cell fate change as well as correlation to expression levels of the three factors. In Aim 2, we will monitor the temporal profiles of these factors through fluorescence protein tagging in single cells, and use the data to evaluate an ensemble of models to identify one or a set of minimal network regulating EMT and G2/M arrest. We will then further examine the roles of individual factors through model analysis and a series of inhibition experiments. Success of the proposed research will provide mechanistic understanding of the regulatory network of cell cycle arrest and EMT in renal epithelial cells. The proposed research is our starting point for an emerging field of quantitative systems biology on kidney fibrosis. We expect that introducing quantitative approaches will greatly accelerate future development of treatment strategies on the increasing global health challenge imposed by progression of fibrosis, which currently lacks effective treatment.